One type of noise found in color digital camera images appears as low frequency, highly colored patterns in regions of high spatial frequency, for example, tweed patterns in clothing. These patterns, called color moiré´ patterns or, simply, color moiré, produce large, slowly varying colored wavy patterns in an otherwise spatially busy region. Color moiré patterns are also referred to as chrominance aliasing patterns, or simply, chrominance aliasing.
There are numerous ways in the prior art for reducing color moiré patterns in digital images. Among these are numerous patents that describe color moiré pattern reduction methods using optical blur filters in digital cameras to avoid aliasing induced color moiré in the first place. However, these blur filters also blur genuine spatial detail in the image that may not be recoverable by subsequent image processing methods.
Some approaches deal specifically with digital image processing methods for reducing or removing chrominance noise artifacts. One class of digital camera patents discloses improvements to the color filter array (CFA) interpolation operation to reduce or eliminate high frequency chrominance noise artifacts. Another class of patents teaches using different pixel shapes (that is, rectangles instead of squares) with accompanying CFA interpolation operations to reduce or eliminate chrominance noise artifacts. However, these techniques address only high frequency chrominance noise, and are generally ineffective against low frequency color moiré.
There is the well known technique in the open literature of taking a digital image with chrominance noise artifacts, converting the image to a luminance-chrominance space, such as CIELAB (CIE International Standard), blurring the chrominance channels and then converting the image back to the original color space. This operation is a standard technique used to combat chrominance noise. One liability with this approach is that there is no discrimination during the blurring step between chrominance noise artifacts and genuine chrominance scene detail. Consequently, sharp colored edges in the image begin to bleed color as the blurring becomes more aggressive. Usually, the color bleed has become unacceptable before most of the low frequency color moiré is removed from the image. Also, if any subsequent image processing is performed on the image, there is the possibility of amplifying the visibility of the color bleeding. A second liability of this approach is that a small, fixed blur kernel is almost required to try to contain the problem of color bleeding. However, to address low frequency color moiré, large blur kernels would be needed to achieve the desired noise cleaning.
Adams, et al, (EP 1202220A2) discloses a method of color artifact reduction that uses adaptive, edge-responsive blur kernels to reduce low frequency color moiré while minimizing color bleeding. While this method addresses most of the concerns previously cited, it is computationally intensive and requires more computational resources than are currently available in most commercial digital cameras today.